The present invention was developed with a meteorological application and will be discussed in that connection in the following. It is to be understood, however, that the invention has other applications as will be appreciated by those knowledgable in the relevant arts. It may be applied wherever a pattern in a data matrix is to be recognized and identified, regardless of the orientation, position or scale of the pattern.
Severe storm events (SSEs) include tornadoes, downbursts (including macrobursts--damaging straight line winds caused by downbursts--wind shear, microbursts), large hail and heavy rains. These events, particularly tornados, may form quickly, vanish suddenly, and may leave behind great damage to property and life. It is therefore of importance to be able to provide some prediction and warning of the occurence of these events.
Weather systems are known to be chaotic in behaviour. Indeed, chaos theory was originally introduced to describe unpredictability in meteorology. The equations that describe the temporal behaviour of weather systems are nonlinear and involve several variables. They are very sensitive to initial conditions. Small changes in initial conditions can yield vast differences in future states. This is often referred to as the "butterfly effect." Consequently, weather prediction is highly uncertain. This uncertainty is likely to be more pronounced when attempting to forecast severe storms, because their structure, intensity and morphology, are presented over a broad spectrum of spatial and temporal scales.
In a storm warning system, problems of prediction originate at the level of storm identification. The uncertainty in initial conditions manifests itself in two distinct forms:
(i) the internal precision and resolution of storm monitoring instruments; and PA1 (ii) the speed at which a storm can be pinpointed. PA1 (i) are inherently suited to function well in environments displaying chaotic behaviour (like the weather); PA1 (ii) can excel at deriving complex decision regions in highly nonlinear and high dimensional data spaces (radar data); PA1 (iii) are capable of generalizing the recognition of previous input patterns (in-sample) to new ones (out-of-sample); PA1 (iv) can extract relevant features from an incomplete or distorted set of data--noisy returns from radar clutter, range folding, and side lobe distortion; PA1 (v) can accelerate the coding of new information (relative to expert and statistical methods) by: PA1 (vi) can process data distributively, making it possible to implement these systems in very high speed parallel computers. PA1 1) dimensional reduction of CAPPI (constant altitude plan position indicator) radar images using data slicing, fragmentation, and classical preprocessing; PA1 2) feature extraction and vector quantization in the form of learned codebooks using self-organizing feature maps (SOFM); and PA1 3) pattern recognition and classification using a back propagation network (BPN) as described in W. Kinsner, A. lndrayanto, and A. Langi, "A study of BP, CPN, and ART Neural Network Models," Proc. 12th Int. Conv. IEEE Eng. in Med. and Biology Soc., IEEE CH2936-3/90, Vol. 3, pp. 1471-1473, 1990. PA1 processing the data matrix with a self organizing network to produce a self organizing feature space mapping; PA1 processing the self organizing feature space mapping to produce a density characterization of the feature space mapping. PA1 self organizing network means for processing the data matrix to produce a self organizing feature space mapping; PA1 density map processing means for processing the self organizing feature space mapping to produce a density characterization of the feature space mapping. PA1 inhibiting the vigilance means with a high activation threshold; PA1 activating the conscience means; and PA1 reducing the threshold for invoking the vigilance means as training proceeds.
Furthermore, the recognition of storm patterns based on local observations is not always possible, since the patterns are inherently temporal in nature, with a sensitive dependence on previous states that may not have been observed.
Real-time recognition and identification of SSE patterns from weather radar imagery have been an instrumental component of operational storm alert systems, serving the military, aerospace, and civilian sectors since the early 1950's. This research theme continues to be among the most difficult, complex, and challenging issues confronting the meteorological community. While weather services around the globe have been improving methods of storm surveillance to facilitate the identification and forecasting of SSEs, the resulting increase in both the size and diversity of the resultant data fields have escalated the difficulty with assimilating and interpreting this information.
Factors at the heart of the problem include:
(i) The life cycle of SSEs is very short, in the order of 10 to 30 minutes. They are often of shorter duration than the opportunity to capture, dissect, and analyze the event on radar, let alone interpret the information.
(ii) Unlike real or physical entities, radar patterns do not manifest themselves in a life-like form, but are mere artifacts that resemble the type of reflectivity return expected from bona fide precipitation distributions accompanying SSEs. The relationship between SSEs and these abstractions is analogous to the correspondence between fire and smoke. Just like smoke can prevail after a fire ceases existence, so can a storm pattern be observed in the wake of a SSE. This time lag interferes with the perception of current conditions.
(iii) The features which do assist in the discrimination of SSE patterns rarely display themselves on a single radar image level, but are present at every level on a three dimensional grid. This complication is attributed to the fact that the severity of a storm is a function of buoyancy, the potential energy available to lift a parcel air and initiate convection. Since buoyancy is maximized during SSEs, the convective currents initiated give rise to non-uniform precipitation distributions at various altitudes. Furthermore, since feature structure (pattern boundaries) in the high dimensional data of radar imagery is usually quite sparse, most of the data is redundant. As such, it will likely require an extensive amount of visual processing to extract a sufficient number of features to secure class separability.
(iv) Distinctive SSE signatures: bow; line; hook; and (B)WER, have been universally accepted as indicators of specific storm features: squall lines; strong rotating updrafts; downbursts; and storm tilt. However, their tremendous spatial and temporal variability through translation, rotation, scale, intensity and structure, give rise to non-linear and multiple attendant mappings in the radar image domain, often resulting in two very different events being perceived as one and the same pattern.
(v) Often some of the most severe SSEs, tornadoes and macrobursts, do not visually present themselves on radar reflectivity (Z) imagery, since they occur in the virtual absence of precipitation. Any weak Z patterns displayed are usually buried in noise: radar clutter; side-lobe distortion; and range folding, causing subtle but distinguishing features to be obscured and overlooked.
(vi) The human brain is not conditioned to recognize SSE patterns. This is a complex task at least as difficult to learn as facial and object identification, and speech recognition.
As difficult as the human act of SSE recognition may seem, the more perplexing issue is to translate this process into the algorithmic and machine domain. To date, most approaches to this problem have relied on traditional artificial intelligence (Al) technology, with emphasis on two paradigms: (i) statistical methods; and (ii) artificial rule based experts. W. R. Moninger, "The Artificial Intelligence Shootout: A comparison of Severe Storm Forecasting Systems," Proc. 16th Conf. on Severe Local Storms, Kananaskis Park, Alta., Canada, Amer. Meteor. Soc., pp. 1-6, 1990 provides a comparative analysis of the implementation of such models in thunderstorm identification systems. K. C. Young, "Quantitative Results for Shootout-89," Proc. 16th Conf. on Severe Local Storms, Kananaskis Park, Alta., Canada, Amer. Meteor. Soc., pp. 112-115, 1990. elaborates on this study with some quantitative results.
These systems are unnatural in terms of their pattern encoding mechanisms. They make false assumptions about the underlying processes in question and require explicit knowledge, massive amounts of memory or extensive processing to encode, recall, and maintain information.
Statistical methods either make Gaussian assumptions or require a priori information about the underlying distribution of the pattern classes. Since there is insufficient information to fully express the relationships between radar patterns and SSEs, this technique produces unsatisfactory results.
Artificial experts, which rely on the use of explicit rules to emulate the qualitative reasoning and subjective analysis skills of a trained expert, are not appropriate because the nonlinear behaviour of SSEs gives rise to non-explicit descriptions of these relationships.
What is needed is a system that is capable of learning what it needs to know about a particular problem, without prior knowledge of an explicit solution, one which can be incrementally trained to extract and generate its own pattern features from exposure to real time quantitative radar data (stimuli). This type of system, commonly referred to as an artificial neural network (ANN) has been a focus of attention in the AI community for several years, but it was not until recently that ANNs have been applied successfully to solve real-world problems, such as speech recognition, three dimensional object identification and financial forecasting.
There are several other facets that make ANNs a very attractive approach for storm identification, namely, they:
(a) adapting in response to changes in the environmental stimuli; and PA2 (b) allowing details of its structural connections to be specified by the network's input correlation history; and
McCann at the National Severe Storms Forecast Center was one of the first to demonstrate the effectiveness of ANNs in an operational storm alert system reported in D. W. McCann, "A Neural Network Short-Term Forecast of Significant Thunderstorms," Weather and Forecasting, Vol. 7, pp. 525-534, 1992. His research included both the training of two back propagation ANNs (BPNs), to forecast significant thunderstorms from fields of surface-based lifted index and surface moisture convergence, as well as combining their results into a single hourly product, to enhance the meteorologist's pattern analysis skills. While this approach does not directly address the issue of identifying specific SSEs from high dimensional radar imagery, it is taken that the success of ANNs in a real-time storm environment depends on the computer power available to scale up from small networks and low-dimensional "toy" problems to massive networks of several thousands or millions of nodes and high-dimensional data. Other applications of ANNs in meteorology have also been limited to using low dimensional raw, unstructured data and a single BPN. These include:
Rainfall forecasting from satellite imagery in T. Chen and M. Takagi, "Rainfall Prediction of Geostationary Meteorological Satellite Images Using Artificial Neural Network," IGARSS, Vol. 2, pp. 1247-1249, 1993, and M. N. French, W. F. Krajewski, and R. R. Cuykendall, "Rainfall Forecasting in Time Using a Neural Network," Journal of Hydrology, Vol. 137, pp. 1-31, 1992;
The prediction of lightning strikes, and most recently, weather radar image prediction in K. Shinozawa, M. Fiji, and N. Sonehara, "A weather radar image prediction method in local parallel computation," Proc. of the Int. Conf. on Neural Networks, Vol. 7, pp. 4210-4215, 1994; and
The diagnosis of tornadic and sever-weather-yielding storm-scale circulations in C. Marzban and G. J. Stumpf, "A Neural Network for the Diagnosis of Tornadic and Severe-weather-yielding Storm-scale Circulations," Submitted to the AMS 27th Conference on Radar Meteorology, Vail Colo.
Research reported in A. Langi, K. Ferens, W. Kinsner, T. Kect, and G. Sawatzky, "Intelligent Storm Identification System Using a Hierarchical Neural Network," WESCANEX '95, pp. 1-4, Nov. 30, 1994 and conducted in conjunction with the University of Manitoba (TR Labs), InfoMagnetics Technologies Corporation (IMT), and the Atmospheric Environment Services (AES) of Environment Canada, have demonstrated that by combining classical image processing with ANNs in a hierarchical configuration, there is no longer a need for scaling up to a massive single ANN when confronted with high dimensional data, such as radar imagery. Their approach decomposes the problem of storm identification into three levels of data processing:
The present invention relates to certain improvements in a system of this latter type. The present HANN storm identification system makes use of the processing stages of the prior art and incorporates additional levels of hierarchy with a more sophisticated and interactive engine of ANNs and training mechanisms.
The attributes which are most important in a real-time adaptive storm identification system include:
(i) Real-Time/High-Dimensional Data Processing:
The surveillance of high-dimensional radar precipitation imagery (up to 481.times.481 pixels) on a continuous and short term basis (.ltoreq.5 min.) demands that the system not only be capable of processing data of such magnitude, but also in a sufficiently short time to give the meteorologist the opportunity to observe the displayed pattern before the next radar signal is captured.
(ii) Non-Stationary/Real-Time Adaptable Knowledge Resource
Since SSEs are governed by air transfer mechanisms,--bouyancy, convection--which are nonstationary and unpredictable in nature, these variable characteristics are ultimately reflected in the radar image. Therefore, the system should be capable of continuously adapting to focus on those features in the radar images which are most prevalent in the dynamic environment. This requirement gives rise to the need for a self-stabilization mechanism in the system.
(iii) Self-Stabilization:
With radar image sizes as large as 481.times.481 pixels, the number of permutations of SSE patterns that can potentialy occur within the image space can exceed 10.sup.6 .times.10.sup.5. The vast size of this space coupled with the inherent variability of SSE patterns can lead to temporal instability. When the number of inputs exceeds the internal storage capacity of the system, novel patterns can only be learned at the expense of destabilizing prior knowledge, eliminating previously learned patterns. Therefore, the tendency of the system to adapt to novel inputs must be either inhibited by a supervisor or self-stabilized to allow for the future encoding of arbitrarily many inputs of any complexity.
(iv) Compact Representation of Information Resource
Since the environment is constantly changing, there is insufficient opportunity to perform exhaustive information searches in the event that a demand forcecast is requested. Therefore, the system should be capable of encoding information in a compact format to facilitate data retrieval and fast "best guess" approximations at any instant.
(v) Self-Organization:
The subjectivity, uncertainty, and incompleteness of current SSE models, calls for a system that can self-organize its recognition code--a direct and unsupervised interaction with the input environment, which causes the system to adaptively assume a form that best represents the structure of the input vectors.
(vi) Data Abstraction/Noise Immunity
The system should be capable of extracting and recognizing relevant information from: (a) redundant data; (b) incompletely specified data eg. data corrupted by noise; and (c) unspecifiable data which does not independently reflect the class to which it belongs. To prevent these artifacts from obscuring the effect of more distinguishing features, the system should employ models which are highly tolerant and immune to noise.
(vii) Nonlinear Behavior:
The system should be capable of deriving arbitrarily complex decision regions in highly nonlinear data, because, many of the the relationships describing the dynamic and spatial behavior between SSEs and attendent radar patterns, are subtle, non-explicit, non-linear, and at times chaotic.
(viii) Specialization and Generalization
The system should be capable of balancing its representation of the input environment, in terms of both local and global details. In a storm environment, there is a strong correlation between the presence of local SSE patterns on radar and the global structure of the complex in which they form. For example, the formation of a tornado is correlated with the spatial organization of hail and rain.
(xi) Ergonomic User Interface:
The system should be capable of interacting with the user in an ergonomic fashion. The output produced by the system should be displayed in a consistent format that can be interpreted quickly, accurately, and reliably.